Scientific discovery requires new ideas to be situated within an ever-expanding landscape of existing knowledge. An emerging challenge is how to identify conceptually relevant prior work from rapidly growing literature, and assess how a new idea differentiates from existing research. Current embedding approaches typically conflate distinct conceptual aspects into single representations and cannot support fine-grained retrieval; meanwhile, LLM-based evaluators are subject to sycophancy biases, failing to provide discriminative novelty assessment. We introduce the Ideation Space, a structured representation that decomposes scientific knowledge into three distinct dimensions learned through contrastive training. Our framework enables principled measurement of conceptual distance between ideas and modeling of ideation transitions that capture the logical connections within an idea. Building upon this representation, we propose a Hierarchical Sub-Space Retrieval framework for efficient, targeted literature retrieval, and a Decomposed Novelty Assessment algorithm that identifies which aspects of an idea are novel. Extensive experiments demonstrate substantial improvements, achieving Recall@30 of 0.329 (16.7% over the strongest baseline), and novelty assessment achieves promising correlation with expert judgments, outperforming all open-source LLM baselines up to 70B.

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